Explanation-Based Learning of Action Models

Diego Aineto, Sergio Jiménez Celorrio, Eva Onaindia

Abstract: The paper presents a classical planning compilation for learning STRIPS action models from partial observations of plan executions. The compilation is flexible to different amounts and types of input knowledge, from learning samples that comprise partially observed intermediate states of the plan execution to samples in which only the initial and final states are observed. The compilation accepts also partially specified action models and it can be used to validate whether an observation of a plan execution follows a given STRIPS action model, even if the given model or the given observation is incomplete.